We next consider a latent Gaussian process model, building on early work on Gaussian processes  [1]}, GP latent variable models [2]}, GP state space models [3]}, and extensions of GPs to general likelihoods [4]}. Our proposed GGP model uses GPs to capture correlations between latent values \(f_{1:T}\) . Again, this is an instance of the generative model \(p(f_{1:T}, y_{1:T})\)  in Eq. (REF ), with a GP for the prior over \(f_{1:T}\) :
\(f_{1:T} \sim \text{GP}( m_{\alpha }(t), k_{\alpha }(t,t^{\prime }) )\) 
